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ICMCS
2005
IEEE
113views Multimedia» more  ICMCS 2005»
16 years 18 days ago
HMM-Based Deception Recognition from Visual Cues
Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. This research effort attempts to leverage automated systems to augment human...
Gabriel Tsechpenakis, Dimitris N. Metaxas, Mark Ad...
CVPR
2011
IEEE
15 years 3 months ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...
ICCV
2011
IEEE
14 years 7 months ago
Human Action Recognition by Learning Bases of Action Attributes and Parts
In this work, we propose to use attributes and parts for recognizing human actions in still images. We define action attributes as the verbs that describe the properties of human...
Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy La...
209
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PAMI
2012
13 years 9 months ago
Holistic Context Models for Visual Recognition
— A novel framework to context modeling, based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availabili...
Nikhil Rasiwasia, Nuno Vasconcelos
ICCV
2009
IEEE
16 years 12 months ago
Evaluating Information Contributions of Bottom-up and Top-down Processes
This paper presents a method to quantitatively evaluate information contributions of individual bottom-up and topdown computing processes in object recognition. Our objective is...
Xiong Yang, Tianfu Wu, Song-Chun Zhu